We consider two federated learning algorithms for training partially personalized models, where the shared and personal parameters are updated either simultaneously or alternately on the devices. Both algorithms have been proposed in the literature, but their convergence properties are not fully understood, especially for the alternating variant. We provide convergence analyses of both algorithms in the general nonconvex setting with partial participation and delineate the regime where one dominates the other. Our experiments on real-world image, text, and speech datasets demonstrate that (a) partial personalization can obtain most of the benefits of full model personalization with a small fraction of personal parameters, and, (b) the alternating update algorithm often outperforms the simultaneous update algorithm by a small but consistent margin.

翻译：我们考虑两种结合式学习算法,用于培训部分个性化模型,即同时或交替更新共享和个人参数。两种算法都是在文献中提议的,但并没有充分理解其趋同特性,特别是对交替变量而言。我们提供对一般非中央结构中两种算法的趋同分析,有部分参与,并划定一个在另一个中占主导地位的制度。我们在现实世界图像、文本和语音数据集方面的实验表明:(a)部分个性化可以取得完全模型个性化的大部分好处,只有一小部分个人参数,以及(b)交替更新算法往往比同时更新算法的微小但始终不变的差差。